J. Semicond. > 2025, Volume 46 > Issue 1 > 011603
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REVIEWS

Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment

Feifei Yin1, Jian Chen2, Haiying Xue1, Kai Kang1, Can Lu1, Xinyi Chen1 and Yang Li3,

+ Author Affiliations

 Corresponding author: Yang Li, yang.li@sdu.edu.cn

DOI: 10.1088/1674-4926/24080026CSTR: 32376.14.1674-4926.24080026

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Abstract: Heart rate variability (HRV) that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system (ANS) has attracted considerable attention. However, traditional electrocardiogram (ECG) devices for HRV analysis are bulky, and hard wires are needed to attach measuring electrodes to the chest, resulting in the poor wearable experience during the long-term measurement. Compared with that, wearable electronics enabling continuously cardiac signals monitoring and HRV assessment provide a desirable and promising approach for helping subjects determine sleeping issues, cardiovascular diseases, or other threats to physical and mental well-being. Until now, significant progress and advances have been achieved in wearable electronics for HRV monitoring and applications for predicting human physical and mental well-being. In this review, the latest progress in the integration of wearable electronics and HRV analysis as well as practical applications in assessment of human physical and mental health are included. The commonly used methods and physiological signals for HRV analysis are briefly summarized. Furthermore, we highlighted the research on wearable electronics concerning HRV assessment and diverse applications such as stress estimation, drowsiness detection, etc. Lastly, the current limitations of the integrated wearable HRV system are concluded, and possible solutions in such a research direction are outlined.

Key words: wearable electronics, HRV analysis, physical and mental well-being, machine learning, stress detection



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Fig. 1.  (Color online) Schematic showing the wearable electronics that acquire human physical signals including ECG, PPG, BCG, and radial pulse for HRV time-domain, frequency-domain, and non-linear analysis, as well as their applications in the assessment of human physical and mental well-being with the help of ML algorithms.

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Fig. 2.  (Color online) (a) Image showing that the electrodes are attached onto the chest of the subjects via wires for the traditional ECG measurement. Reproduced with permission[71]. Copyright 2011, Springer. (b) Recorded ECG signal showing the P−QRS−T heartbeat waveform. Reproduced with permission[72]. Copyright 2022, Elsevier Ltd. (c) PPG working mechanism. (d) Two operation modes for PPG devices: transmission mode and reflection mode. Reproduced with permission[56]. Copyright 2018, IEEE. (e) Recorded PPG signal showing the obvious diastole and systole features. Reproduced with permission[73]. Copyright 2020, IEEE. (f) Recorded BCG signal that could be comparable with ECG signal. Reproduced with permission[62]. Copyright 2022, IEEE. (g) Recorded radial pulse signal showing obvious "P", "T", and "D" waves. Reproduced with permission[74]. Copyright 2024, Elsevier Ltd. (h) Schematic of IBIs that are the time intervals between every two neighboring P peaks. Reproduced with permission[70]. Copyright 2023, Elsevier Ltd.

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Fig. 3.  (Color online) (a) Top-view and bottom-view of the proposed cardiopulmonary monitoring system where the printed the Cu electrodes as the part of the copper traces of the FPCB for the ECG measurement. Reproduced with permission[80]. Copyright 2024, IEEE. (b) Two typical and similar three-step processes for the fabrication of graphene textile electrodes: The one involves "dipping-dry-reduce", while the other includes "printing-dry-reduce". Reproduced with permission[72]. Copyright 2022, Elsevier Ltd. (c) The process flow chart for the HRV calculation and assessment through the measured ECG signal. Reproduced with permission[81]. Copyright 2024, IEEE. Calculated HRV features of (d) time-domain, (e) frequency-domain, and (f) non-linear measures. Reproduced with permission[83]. Copyright 2023, Elsevier Ltd.

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Fig. 4.  (Color online) (a) Detection of all the true peaks (P) and extraction of P−P intervals from PPG signals for HRV time-domain and frequency-domain analysis. Reproduced with permission[93]. Copyright 2022, Elsevier Ltd. (b) Schematic of the PPG module integrated in a wearable patch. (c) Measured PPG signals by the wearable patch. (d) Detected peaks of the PPG signal with the help of peak-finding algorithms and the calculated HR and HRV values. Reproduced with permission[94]. Copyright 2024, Wiley-VCH. (e) Schematic of the OPT part of the epidermal and flexible hybrid PPG sensor. (f) Process flow for assembling the hybrid PPG sensor onto the fingertip. Reproduced with permission[96]. Copyright 2017, Wiley-VCH.

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Fig. 5.  (Color online) (a) Photograph showing the simultaneous collection of ECG, BCG, and pulse wave signals from a subject through ECG, BCG, and piezoelectric sensors. (b) Collected BCG, ECG, and pulse wave signals. Reproduced with permission[99]. Copyright 2022, MDPI. (c) Calculated HRV (SDNN) results of the subject under different conditions including the control, specific breathing pattern, walking, and running groups, respectively. Reproduced with permission[100]. Copyright 2023, IEEE. (d) Experiment set-up for ECG and BCG measurements, including data acquisition, transmission, processing, calculation, and display. (e) Time-domain, (f) frequency-domain, and (g) non-linear HRV results of a healthy young subject by using the ECG and BCG signals at 0.1 and 0.25 Hz respiration rate. Reproduced with permission[101]. Copyright 2023, IEEE.

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Fig. 6.  (Color online) (a) Schematic of the working principle for a capacitive pulse sensor attached to the skin surface directly. Reproduced with permission[104]. Copyright 2023, IEEE. (b) Illustration showing a wearable sensor array for the multi-channel acquisition of arterial pulse signals. (c) Schematic of the structure of the multi-channel wearable sensor array. Reproduced with permission[74]. Copyright 2024, Elsevier Ltd. (d) Measured invasive blood pressure signal by the catheter as well as the pressure wire, and the non-invasive pulse signal by a TUNES (waveform details on each cardiac cycle of the measured signals are magnified). (e) Calculated HRV time-domain, frequency-domain, and non-linear measures in three different conditions including sleeping, sitting, and cycling. Reproduced with permission[63]. Copyright 2024, The authors. (f) Optical image showing that the PVA-based TENG was applied to detect human pulse signals on the wrist. (g) Measured pulse signals by the PVA-based TENG and (h) the correspondingly magnified waveform of one pulse period. (i) Extracted peak-to-peak intervals through the PVA-based TENG. Reproduced with permission[107]. Copyright 2020, Wiley-VCH.

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Fig. 7.  (Color online) (a) Schematic diagram showing the regulation and influence of human mental stress on the ANS activity and HRV levels, respectively. Reproduced with permission[112]. Copyright 2021, The Authors. (b) A mental stress detection system that integrates a single lead ECG collector into a wearable smart T-shirts. Reproduced with permission[79]. Copyright 2022, MDPI. (c) Diagram of the SFB system involving the flexible printed circuit board (FPCB), power supply, battery, charging port, switch, and the electrodes. (d) Details on the dimension of the designed stretchable electrodes and inter-connectors and their (e) FEA on the stretchability under the tensile strain of 30%. Reproduced with permission[124]. Copyright 2023, The Authors.

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Fig. 8.  (Color online) (a) Flow chart of the training and testing processes of ML methods used for human HRV related prediction. (b) Schematic of a typical CNN model for human stress classification. Reproduced with permission[128]. Copyright 2020, The Authors. Confusion matrices showing the classification performance of the CNN-LSTM algorithm using the HRV (c) time- and (d) frequency-domain features. Reproduced with permission[129]. Copyright 2020, The Authors. (e) Schematic diagram of the 4-layer MLP network. (f) Confusion matrix showing performance of the MLP network for emotion classification. Reproduced with permission[70]. Copyright 2023, Elsevier Ltd. (g) Schematic of a typical RF algorithm. (h) Classification accuracy of the RF algorithm using features of the pulse wave data such as peak-to-peak value. Reproduced with permission[63]. Copyright 2023, The Authors. (i) Schematic of a typical SVM model. Reproduced with permission[112]. Copyright 2024, The Authors. (j) Confusion matrix showing the accuracy of the SVM for classifying different OSA events. Reproduced with permission[123]. Copyright 2023, IEEE.

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Table 1.   HRV parameters in time-domain analysis.

Parameters (Unit) Description
SDNN (ms) Standard deviation of "R−R" intervals of normal sinus beats
SDANN (ms) Standard deviation of the average "R−R" intervals for each 5 min segment of a 24 h HRV recording
SDSD (ms) Standard deviation of the adjacent "R−R" interval differences
RMSSD (ms) Root mean square of the successive differences between the adjacent "R−R" time intervals
NN50 Number of successive "R−R" intervals that exceeds 50 ms
pNN50 (%) Proportion of successive "R−R" intervals that exceeds 50 ms
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Table 2.   HRV parameters in frequency-domain analysis.

Parameters (Unit) Description
ULF power (ms2) Absolute power of the ULF band (≤0.003 Hz)
VLF power (ms2) Absolute power of the VLF band (0.003−0.04 Hz)
LF power (ms2) Absolute power of the LF band (0.04−0.15 Hz)
HF power (ms2) Absolute power of the HF band (0.15−0.4 Hz)
LF/HF (%) Ratio of LF to HF power
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Table 3.   HRV parameters in non-linear analysis.

Parameters (Unit) Description
S (ms) Area of the fitted ellipse representing the total HRV level
SD1 (ms) Semi-minor axes of the fitted ellipse
SD2 (ms) Semi-major axes of the fitted ellipse
SD1/SD2 (%) Ratio of SD1 to SD2
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Table 4.   Summary of HRV parameters achieved by various wearable electronics.

Types of
wearable devices		 HRV analysis methods Application Ref.
Time-domain Frequency-domain Non-linear
ECG RMSSD, NN50, pNN50 − − Mental stress detection 79
RMSSD, SDNN − − Detection of cardiac arrhythmias 80
Intervals distribution ULF, VLF, LF, HF, VHF SD1, SD2 − 83
SDANN, RMSSD, SDSD − − − 85
PPG SDNN, RMSSD, pNN50 LF, HF, LF/HF − Cardiovascular disease 34
SDNN, pNN50 LF, HF, LF/HF − Drowsiness detection 56
SDNN LF, HF, VLF − − 73
RMSSD − − Stress detection 91
RMSSD − − Balance of ANS 94
BCG SDNN LF/HF − Fatigue detection 98
SDNN, RMSSD, pNN50 LF, HF, LF/HF − − 99
SDNN − − − 100
RMSSD, SDSD, SDNN, pNN50 LF, HF, VLF SD1, SD2, SD1/ SD2 RSA 101
Radial pulse RMSSD − − AFib Monitoring 104
SDNN − − Cardiac function assessment 71
SDNN − − − 107
DownLoad: CSV
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    Received: 17 August 2024 Revised: 25 September 2024 Online: Accepted Manuscript: 22 October 2024Uncorrected proof: 05 December 2024Published: 15 January 2025

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      Article Navigation >  Journal of Semiconductors  > 2025  >  46(1): 011603
      Feifei Yin, Jian Chen, Haiying Xue, Kai Kang, Can Lu, Xinyi Chen, Yang Li. Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment[J]. Journal of Semiconductors, 2025, 46(1): 011603. doi: 10.1088/1674-4926/24080026 ****F F Yin, J Chen, H Y Xue, K Kang, C Lu, X Y Chen, and Y Li, Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment[J]. J. Semicond., 2025, 46(1), 011603 doi: 10.1088/1674-4926/24080026
      Citation:
      Feifei Yin, Jian Chen, Haiying Xue, Kai Kang, Can Lu, Xinyi Chen, Yang Li. Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment[J]. Journal of Semiconductors, 2025, 46(1): 011603. doi: 10.1088/1674-4926/24080026 ****
      F F Yin, J Chen, H Y Xue, K Kang, C Lu, X Y Chen, and Y Li, Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment[J]. J. Semicond., 2025, 46(1), 011603 doi: 10.1088/1674-4926/24080026
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      Integration of wearable electronics and heart rate variability for human physical and mental well-being assessment

      DOI: 10.1088/1674-4926/24080026
      CSTR: 32376.14.1674-4926.24080026
      • 1.

        East China Institute of Photo-Elextro ICs, Suzhou 215163, China

      • 2.

        Bengbu Representative Department, Bengbu 233000, China

      • 3.

        School of Integrated Circuits, Shandong University, Jinan 250101, China

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      • Feifei Yin received her Master's degree in the School of Information Science and Engineering from the University of Jinan, China in 2021. She finished her Ph.D. dgree in the Department of Electronics Engineering at Kwangwoon University in 2024, South Korea. Now, she is a researcher working in the East China Institute of Photo-Elextro ICs, China. Her current research focuses on gas sensing devices and flexible electronic devices
      • Yang Li has been a professor at the School of Microelectronics at the Shandong University, China since 2023. He received his Ph.D. degree in the Department of Electronics Engineering at Kwangwoon University, South Korea in 2015. He conducted his Postdoc research in the department of Electronic Engineering at Kwangwoon University, Korea from 2015 to 2016. He has published over 100 peer-reviewed journal papers and has been authorized for over 40 Chinese and Korean patents. His research interests include advanced semiconductor fabrication, nanostructured flexible materials, gas sensors, and memristors
      • Corresponding author: yang.li@sdu.edu.cn
      • Received Date: 2024-08-17
      • Revised Date: 2024-09-25
        • Available Online: 2024-10-22

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